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Multiregion image segmentation by parametric kernel graph cuts.

Mohamed Ben Salah1, Amar Mitiche, Ismail Ben Ayed

  • 1Institut National de la Recherche Scientifique (INRS-EMT), Montréal, Quebec, Canada. bensalah@emt.inrs.ca

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|August 19, 2010
PubMed
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This study introduces kernel mapping for multiregion graph cut image partitioning. This method effectively segments images by adapting data for graph cut models, offering an alternative to complex image modeling.

Area of Science:

  • Computer Vision
  • Image Processing
  • Computational Mathematics

Background:

  • Image partitioning is crucial for image analysis.
  • Traditional graph cut methods often require specific data models.
  • Complex image data can pose challenges for existing segmentation techniques.

Purpose of the Study:

  • To investigate multiregion image partitioning using kernel mapping with graph cuts.
  • To adapt image data for piecewise constant models applicable to graph cut formulations.
  • To develop an effective alternative to complex image modeling in segmentation.

Main Methods:

  • Implicitly transforming image data using kernel functions.
  • Employing a graph cut formulation with data and regularization terms.

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  • Iterative energy minimization through graph cut and fixed point computations.

    • Main Results:

    • Demonstrated effectiveness on synthetic and natural image datasets.
    • Quantitative and comparative performance assessments were conducted.
    • Successful application to diverse real-world image types including medical and SAR images.

    Conclusions:

    • Kernel mapping provides an effective approach for multiregion graph cut image partitioning.
    • The method simplifies modeling while leveraging graph cut computational advantages.
    • The technique shows broad applicability across various imaging domains.